Strategies to Mitigate COVID-19 Resurgence Assuming Immunity Waning: A Study for Karnataka, India

  1. Aniruddha Adiga
  2. Siva Athreya
  3. Bryan Lewis
  4. Madhav V. Marathe
  5. Nihesh Rathod
  6. Rajesh Sundaresan
  7. Samarth Swarup
  8. Srinivasan Venkatramanan
  9. Sarath Yasodharan

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Abstract

COVID-19 vaccination is being rolled out among the general population in India. Spatial heterogeneities exist in seroprevalence and active infections across India. Using a spatially explicit age-stratified model of Karnataka at the district level, we study three spatial vaccination allocation strategies under different vaccination capacities and a variety of non-pharmaceutical intervention (NPI) scenarios. The models are initialised using on-the-ground datasets that capture reported cases, seroprevalence estimates, seroreversion and vaccine rollout plans. The three vaccination strategies we consider are allocation in proportion to the district populations, allocation in inverse proportion to the seroprevalence estimates, and allocation in proportion to the case-incidence rates during a reference period.

The results suggest that the effectiveness of these strategies (in terms of cumulative cases at the end of a four-month horizon) are within 2% of each other, with allocation in proportion to population doing marginally better at the state level. The results suggest that the allocation schemes are robust and thus the focus should be on the easy to implement scheme based on population. Our immunity waning model predicts the possibility of a subsequent resurgence even under relatively strong NPIs. Finally, given a per-day vaccination capacity, our results suggest the level of NPIs needed for the healthcare infrastructure to handle a surge.

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    SciScore for 10.1101/2021.05.26.21257836: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    We then discuss limitations of our study arising from model errors, uncertainty in the factors driving the resurgence in Karnataka, policy changes in response to the local circumstances, and vaccine supply constraints. 4.1 Discussion of the results: The significant second surge has highlighted widespread susceptibility in early 2021. This could be due to waning immunity, or limited spread of the first wave, or novel variants, or a combination thereof. An expedited, effective, and equitable vaccine campaign remains the only feasible pathway to controlling COVID-19 in India. Given supply constraints and India’s population size, tools for designing efficient vaccination campaigns are essential. In this paper, we developed a model (using serosurvey data and on-the-ground datasets) to study vaccination allocation strategies and the interplay of vaccination capacity and NPIs in achieving sufficient immunity levels. The main messages of this work are the following: Our model makes use of several real-time data sources – population data, age-distribution, age-stratified contact rates, disease progression data, confirmed case trajectories across units, seroprevalence data, time series of tests conducted, vaccination time-series, and efficacy of vaccines. Our compartmental ODE model used patches that where made up of age and unit stratifications. We modelled mobility across subunits to be independent of age. A future extension could be to use age-stratified mobility data. There are man...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

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    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
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  2. Version published to 10.1101/2021.05.26.21257836v1 on medRxiv